{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HF_WTA.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyO4Ua4/JOI7C9V5CqU8zpCO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Oil-and-Gas-data-analysis/blob/master/HF_WTA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BblFHQI8f7xt",
        "colab_type": "text"
      },
      "source": [
        "#Analyzing a Hydraulically Fractured Well with Python.\n",
        "\n",
        "Background : Pressure Transient Analysis\n",
        "\n",
        "Divyanshu Vyas | dvyas13ad@gmail.com\n",
        "\n",
        "Domain : RE | PE | Machine Learning | Deep Learning | Simulation\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "--aoao8KH9Hb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1yVweLKAJvuH",
        "colab_type": "text"
      },
      "source": [
        "![image.png]()"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jt_cl4ywJ7Mn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Pi = 5000 #psi\n",
        "\n",
        "q = 2000 #stb/d\n",
        "\n",
        "h = 50 #ft\n",
        "\n",
        "phi = 0.24\n",
        "\n",
        "ct = 14.8E-6\n",
        "\n",
        "Bo = 1.5 #rb/stb\n",
        "\n",
        "mu = 0.3 #cp, oil\n",
        "\n",
        "rw = 0.29 #ft"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNS_5EToISe6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('hf.txt',sep='\\t')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fi_lh4NgJFnW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['t_1/2'] = np.sqrt(df['t'])\n",
        "df['t_1/4'] = np.sqrt(df['t_1/2'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1RRfkHTJGdM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "4ea8b004-0d1d-4845-8622-a1d05d1ab733"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>FBHP (psia)</th>\n",
              "      <th>t_1/2</th>\n",
              "      <th>t_1/4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.01002</td>\n",
              "      <td>4980.61</td>\n",
              "      <td>0.100100</td>\n",
              "      <td>0.316386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.01209</td>\n",
              "      <td>4978.68</td>\n",
              "      <td>0.109955</td>\n",
              "      <td>0.331594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.01420</td>\n",
              "      <td>4976.57</td>\n",
              "      <td>0.119164</td>\n",
              "      <td>0.345201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.01730</td>\n",
              "      <td>4974.24</td>\n",
              "      <td>0.131529</td>\n",
              "      <td>0.362670</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         t  FBHP (psia)     t_1/2     t_1/4\n",
              "0  0.00000      5000.00  0.000000  0.000000\n",
              "1  0.01002      4980.61  0.100100  0.316386\n",
              "2  0.01209      4978.68  0.109955  0.331594\n",
              "3  0.01420      4976.57  0.119164  0.345201\n",
              "4  0.01730      4974.24  0.131529  0.362670"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ql00i8hsJY7X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "b6af761f-821e-40b2-af56-ddf5081a9301"
      },
      "source": [
        "plt.plot(df['t_1/2'] , df['FBHP (psia)'])\n",
        "plt.plot(df['t_1/4'] , df['FBHP (psia)'])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f607ae42438>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-K7NHKxKJfZo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['dP'] = np.zeros(len(df))\n",
        "df['dt'] = np.zeros(len(df))"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03lIvbx1Ncwg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['dP'] = Pi - df['FBHP (psia)']\n",
        "\n",
        "# df"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exSURxOxNdqS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['dt'].loc[1:] = df['t'].diff()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LSbwvAWfO-uV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['d(dP)'] = np.zeros(len(df))\n",
        "\n",
        "df['d(dP)'].loc[1:] = df['dP'].diff()\n",
        "\n",
        "df['t*d(dP)/dt'] = np.zeros(len(df))"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5x_CeEW7OioW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['t*d(dP)/dt'].loc[1:] = df['d(dP)']*df['t']/df['dt']"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ntNEh5BGOvNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f3750cdb-93c4-447e-8503-e3566c760734"
      },
      "source": [
        "df"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>t</th>\n",
              "      <th>FBHP (psia)</th>\n",
              "      <th>t_1/2</th>\n",
              "      <th>t_1/4</th>\n",
              "      <th>dP</th>\n",
              "      <th>dt</th>\n",
              "      <th>d(dP)</th>\n",
              "      <th>t*d(dP)/dt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00000</td>\n",
              "      <td>5000.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.01002</td>\n",
              "      <td>4980.61</td>\n",
              "      <td>0.100100</td>\n",
              "      <td>0.316386</td>\n",
              "      <td>19.39</td>\n",
              "      <td>0.01002</td>\n",
              "      <td>19.39</td>\n",
              "      <td>19.390000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.01209</td>\n",
              "      <td>4978.68</td>\n",
              "      <td>0.109955</td>\n",
              "      <td>0.331594</td>\n",
              "      <td>21.32</td>\n",
              "      <td>0.00207</td>\n",
              "      <td>1.93</td>\n",
              "      <td>11.272319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.01420</td>\n",
              "      <td>4976.57</td>\n",
              "      <td>0.119164</td>\n",
              "      <td>0.345201</td>\n",
              "      <td>23.43</td>\n",
              "      <td>0.00211</td>\n",
              "      <td>2.11</td>\n",
              "      <td>14.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.01730</td>\n",
              "      <td>4974.24</td>\n",
              "      <td>0.131529</td>\n",
              "      <td>0.362670</td>\n",
              "      <td>25.76</td>\n",
              "      <td>0.00310</td>\n",
              "      <td>2.33</td>\n",
              "      <td>13.002903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.02090</td>\n",
              "      <td>4971.98</td>\n",
              "      <td>0.144568</td>\n",
              "      <td>0.380221</td>\n",
              "      <td>28.02</td>\n",
              "      <td>0.00360</td>\n",
              "      <td>2.26</td>\n",
              "      <td>13.120556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.02520</td>\n",
              "      <td>4969.20</td>\n",
              "      <td>0.158745</td>\n",
              "      <td>0.398428</td>\n",
              "      <td>30.80</td>\n",
              "      <td>0.00430</td>\n",
              "      <td>2.78</td>\n",
              "      <td>16.292093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.03080</td>\n",
              "      <td>4966.14</td>\n",
              "      <td>0.175499</td>\n",
              "      <td>0.418926</td>\n",
              "      <td>33.86</td>\n",
              "      <td>0.00560</td>\n",
              "      <td>3.06</td>\n",
              "      <td>16.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.03680</td>\n",
              "      <td>4963.17</td>\n",
              "      <td>0.191833</td>\n",
              "      <td>0.437988</td>\n",
              "      <td>36.83</td>\n",
              "      <td>0.00600</td>\n",
              "      <td>2.97</td>\n",
              "      <td>18.216000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.04360</td>\n",
              "      <td>4959.52</td>\n",
              "      <td>0.208806</td>\n",
              "      <td>0.456953</td>\n",
              "      <td>40.48</td>\n",
              "      <td>0.00680</td>\n",
              "      <td>3.65</td>\n",
              "      <td>23.402941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.05260</td>\n",
              "      <td>4955.96</td>\n",
              "      <td>0.229347</td>\n",
              "      <td>0.478902</td>\n",
              "      <td>44.04</td>\n",
              "      <td>0.00900</td>\n",
              "      <td>3.56</td>\n",
              "      <td>20.806222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.06340</td>\n",
              "      <td>4951.59</td>\n",
              "      <td>0.251794</td>\n",
              "      <td>0.501790</td>\n",
              "      <td>48.41</td>\n",
              "      <td>0.01080</td>\n",
              "      <td>4.37</td>\n",
              "      <td>25.653519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.07540</td>\n",
              "      <td>4947.35</td>\n",
              "      <td>0.274591</td>\n",
              "      <td>0.524014</td>\n",
              "      <td>52.65</td>\n",
              "      <td>0.01200</td>\n",
              "      <td>4.24</td>\n",
              "      <td>26.641333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.09100</td>\n",
              "      <td>4942.72</td>\n",
              "      <td>0.301662</td>\n",
              "      <td>0.549238</td>\n",
              "      <td>57.28</td>\n",
              "      <td>0.01560</td>\n",
              "      <td>4.63</td>\n",
              "      <td>27.008333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.11000</td>\n",
              "      <td>4937.66</td>\n",
              "      <td>0.331662</td>\n",
              "      <td>0.575901</td>\n",
              "      <td>62.34</td>\n",
              "      <td>0.01900</td>\n",
              "      <td>5.06</td>\n",
              "      <td>29.294737</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.13200</td>\n",
              "      <td>4932.23</td>\n",
              "      <td>0.363318</td>\n",
              "      <td>0.602759</td>\n",
              "      <td>67.77</td>\n",
              "      <td>0.02200</td>\n",
              "      <td>5.43</td>\n",
              "      <td>32.580000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.15900</td>\n",
              "      <td>4927.05</td>\n",
              "      <td>0.398748</td>\n",
              "      <td>0.631465</td>\n",
              "      <td>72.95</td>\n",
              "      <td>0.02700</td>\n",
              "      <td>5.18</td>\n",
              "      <td>30.504444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.19000</td>\n",
              "      <td>4921.48</td>\n",
              "      <td>0.435890</td>\n",
              "      <td>0.660220</td>\n",
              "      <td>78.52</td>\n",
              "      <td>0.03100</td>\n",
              "      <td>5.57</td>\n",
              "      <td>34.138710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.22900</td>\n",
              "      <td>4915.49</td>\n",
              "      <td>0.478539</td>\n",
              "      <td>0.691765</td>\n",
              "      <td>84.51</td>\n",
              "      <td>0.03900</td>\n",
              "      <td>5.99</td>\n",
              "      <td>35.172051</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.27600</td>\n",
              "      <td>4907.10</td>\n",
              "      <td>0.525357</td>\n",
              "      <td>0.724815</td>\n",
              "      <td>92.90</td>\n",
              "      <td>0.04700</td>\n",
              "      <td>8.39</td>\n",
              "      <td>49.268936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.33200</td>\n",
              "      <td>4900.00</td>\n",
              "      <td>0.576194</td>\n",
              "      <td>0.759075</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0.05600</td>\n",
              "      <td>7.10</td>\n",
              "      <td>42.092857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.39600</td>\n",
              "      <td>4892.37</td>\n",
              "      <td>0.629285</td>\n",
              "      <td>0.793275</td>\n",
              "      <td>107.63</td>\n",
              "      <td>0.06400</td>\n",
              "      <td>7.63</td>\n",
              "      <td>47.210625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>0.47700</td>\n",
              "      <td>4884.14</td>\n",
              "      <td>0.690652</td>\n",
              "      <td>0.831055</td>\n",
              "      <td>115.86</td>\n",
              "      <td>0.08100</td>\n",
              "      <td>8.23</td>\n",
              "      <td>48.465556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.56800</td>\n",
              "      <td>4875.29</td>\n",
              "      <td>0.753658</td>\n",
              "      <td>0.868135</td>\n",
              "      <td>124.71</td>\n",
              "      <td>0.09100</td>\n",
              "      <td>8.85</td>\n",
              "      <td>55.239560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.68500</td>\n",
              "      <td>4865.77</td>\n",
              "      <td>0.827647</td>\n",
              "      <td>0.909751</td>\n",
              "      <td>134.23</td>\n",
              "      <td>0.11700</td>\n",
              "      <td>9.52</td>\n",
              "      <td>55.736752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.83600</td>\n",
              "      <td>4855.52</td>\n",
              "      <td>0.914330</td>\n",
              "      <td>0.956206</td>\n",
              "      <td>144.48</td>\n",
              "      <td>0.15100</td>\n",
              "      <td>10.25</td>\n",
              "      <td>56.748344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.99500</td>\n",
              "      <td>4844.48</td>\n",
              "      <td>0.997497</td>\n",
              "      <td>0.998748</td>\n",
              "      <td>155.52</td>\n",
              "      <td>0.15900</td>\n",
              "      <td>11.04</td>\n",
              "      <td>69.086792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1.20000</td>\n",
              "      <td>4832.60</td>\n",
              "      <td>1.095445</td>\n",
              "      <td>1.046635</td>\n",
              "      <td>167.40</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>11.88</td>\n",
              "      <td>69.541463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1.44600</td>\n",
              "      <td>4819.82</td>\n",
              "      <td>1.202497</td>\n",
              "      <td>1.096584</td>\n",
              "      <td>180.18</td>\n",
              "      <td>0.24600</td>\n",
              "      <td>12.78</td>\n",
              "      <td>75.121463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.74000</td>\n",
              "      <td>4806.06</td>\n",
              "      <td>1.319091</td>\n",
              "      <td>1.148517</td>\n",
              "      <td>193.94</td>\n",
              "      <td>0.29400</td>\n",
              "      <td>13.76</td>\n",
              "      <td>81.436735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>2.10000</td>\n",
              "      <td>4793.43</td>\n",
              "      <td>1.449138</td>\n",
              "      <td>1.203801</td>\n",
              "      <td>206.57</td>\n",
              "      <td>0.36000</td>\n",
              "      <td>12.63</td>\n",
              "      <td>73.675000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>2.50000</td>\n",
              "      <td>4779.98</td>\n",
              "      <td>1.581139</td>\n",
              "      <td>1.257433</td>\n",
              "      <td>220.02</td>\n",
              "      <td>0.40000</td>\n",
              "      <td>13.45</td>\n",
              "      <td>84.062500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>3.02000</td>\n",
              "      <td>4763.17</td>\n",
              "      <td>1.737815</td>\n",
              "      <td>1.318262</td>\n",
              "      <td>236.83</td>\n",
              "      <td>0.52000</td>\n",
              "      <td>16.81</td>\n",
              "      <td>97.627308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>3.63000</td>\n",
              "      <td>4747.75</td>\n",
              "      <td>1.905256</td>\n",
              "      <td>1.380310</td>\n",
              "      <td>252.25</td>\n",
              "      <td>0.61000</td>\n",
              "      <td>15.42</td>\n",
              "      <td>91.761639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4.38000</td>\n",
              "      <td>4731.33</td>\n",
              "      <td>2.092845</td>\n",
              "      <td>1.446667</td>\n",
              "      <td>268.67</td>\n",
              "      <td>0.75000</td>\n",
              "      <td>16.42</td>\n",
              "      <td>95.892800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>5.21000</td>\n",
              "      <td>4713.83</td>\n",
              "      <td>2.282542</td>\n",
              "      <td>1.510809</td>\n",
              "      <td>286.17</td>\n",
              "      <td>0.83000</td>\n",
              "      <td>17.50</td>\n",
              "      <td>109.849398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>6.29000</td>\n",
              "      <td>4698.38</td>\n",
              "      <td>2.507987</td>\n",
              "      <td>1.583663</td>\n",
              "      <td>301.62</td>\n",
              "      <td>1.08000</td>\n",
              "      <td>15.45</td>\n",
              "      <td>89.981944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>7.58000</td>\n",
              "      <td>4678.74</td>\n",
              "      <td>2.753180</td>\n",
              "      <td>1.659271</td>\n",
              "      <td>321.26</td>\n",
              "      <td>1.29000</td>\n",
              "      <td>19.64</td>\n",
              "      <td>115.404031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>9.14000</td>\n",
              "      <td>4661.40</td>\n",
              "      <td>3.023243</td>\n",
              "      <td>1.738748</td>\n",
              "      <td>338.60</td>\n",
              "      <td>1.56000</td>\n",
              "      <td>17.34</td>\n",
              "      <td>101.594615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>11.02000</td>\n",
              "      <td>4643.13</td>\n",
              "      <td>3.319639</td>\n",
              "      <td>1.821988</td>\n",
              "      <td>356.87</td>\n",
              "      <td>1.88000</td>\n",
              "      <td>18.27</td>\n",
              "      <td>107.093298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>13.12000</td>\n",
              "      <td>4623.87</td>\n",
              "      <td>3.622154</td>\n",
              "      <td>1.903196</td>\n",
              "      <td>376.13</td>\n",
              "      <td>2.10000</td>\n",
              "      <td>19.26</td>\n",
              "      <td>120.329143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>15.81000</td>\n",
              "      <td>4607.71</td>\n",
              "      <td>3.976179</td>\n",
              "      <td>1.994036</td>\n",
              "      <td>392.29</td>\n",
              "      <td>2.69000</td>\n",
              "      <td>16.16</td>\n",
              "      <td>94.977546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>19.07000</td>\n",
              "      <td>4586.54</td>\n",
              "      <td>4.366921</td>\n",
              "      <td>2.089718</td>\n",
              "      <td>413.46</td>\n",
              "      <td>3.26000</td>\n",
              "      <td>21.17</td>\n",
              "      <td>123.838006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>21.99000</td>\n",
              "      <td>4568.78</td>\n",
              "      <td>4.689350</td>\n",
              "      <td>2.165491</td>\n",
              "      <td>431.22</td>\n",
              "      <td>2.92000</td>\n",
              "      <td>17.76</td>\n",
              "      <td>133.747397</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>27.72000</td>\n",
              "      <td>4550.26</td>\n",
              "      <td>5.264979</td>\n",
              "      <td>2.294554</td>\n",
              "      <td>449.74</td>\n",
              "      <td>5.73000</td>\n",
              "      <td>18.52</td>\n",
              "      <td>89.594136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>32.99000</td>\n",
              "      <td>4530.93</td>\n",
              "      <td>5.743692</td>\n",
              "      <td>2.396600</td>\n",
              "      <td>469.07</td>\n",
              "      <td>5.27000</td>\n",
              "      <td>19.33</td>\n",
              "      <td>121.005066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>39.77000</td>\n",
              "      <td>4510.79</td>\n",
              "      <td>6.306346</td>\n",
              "      <td>2.511244</td>\n",
              "      <td>489.21</td>\n",
              "      <td>6.78000</td>\n",
              "      <td>20.14</td>\n",
              "      <td>118.136844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>47.94000</td>\n",
              "      <td>4495.11</td>\n",
              "      <td>6.923872</td>\n",
              "      <td>2.631325</td>\n",
              "      <td>504.89</td>\n",
              "      <td>8.17000</td>\n",
              "      <td>15.68</td>\n",
              "      <td>92.007246</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>57.80000</td>\n",
              "      <td>4473.44</td>\n",
              "      <td>7.602631</td>\n",
              "      <td>2.757287</td>\n",
              "      <td>526.56</td>\n",
              "      <td>9.86000</td>\n",
              "      <td>21.67</td>\n",
              "      <td>127.031034</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>68.80000</td>\n",
              "      <td>4456.55</td>\n",
              "      <td>8.294577</td>\n",
              "      <td>2.880031</td>\n",
              "      <td>543.45</td>\n",
              "      <td>11.00000</td>\n",
              "      <td>16.89</td>\n",
              "      <td>105.639273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>82.95000</td>\n",
              "      <td>4433.21</td>\n",
              "      <td>9.107689</td>\n",
              "      <td>3.017895</td>\n",
              "      <td>566.79</td>\n",
              "      <td>14.15000</td>\n",
              "      <td>23.34</td>\n",
              "      <td>136.823534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>97.50000</td>\n",
              "      <td>4408.86</td>\n",
              "      <td>9.874209</td>\n",
              "      <td>3.142325</td>\n",
              "      <td>591.14</td>\n",
              "      <td>14.55000</td>\n",
              "      <td>24.35</td>\n",
              "      <td>163.170103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           t  FBHP (psia)     t_1/2  ...        dt  d(dP)  t*d(dP)/dt\n",
              "0    0.00000      5000.00  0.000000  ...   0.00000   0.00    0.000000\n",
              "1    0.01002      4980.61  0.100100  ...   0.01002  19.39   19.390000\n",
              "2    0.01209      4978.68  0.109955  ...   0.00207   1.93   11.272319\n",
              "3    0.01420      4976.57  0.119164  ...   0.00211   2.11   14.200000\n",
              "4    0.01730      4974.24  0.131529  ...   0.00310   2.33   13.002903\n",
              "5    0.02090      4971.98  0.144568  ...   0.00360   2.26   13.120556\n",
              "6    0.02520      4969.20  0.158745  ...   0.00430   2.78   16.292093\n",
              "7    0.03080      4966.14  0.175499  ...   0.00560   3.06   16.830000\n",
              "8    0.03680      4963.17  0.191833  ...   0.00600   2.97   18.216000\n",
              "9    0.04360      4959.52  0.208806  ...   0.00680   3.65   23.402941\n",
              "10   0.05260      4955.96  0.229347  ...   0.00900   3.56   20.806222\n",
              "11   0.06340      4951.59  0.251794  ...   0.01080   4.37   25.653519\n",
              "12   0.07540      4947.35  0.274591  ...   0.01200   4.24   26.641333\n",
              "13   0.09100      4942.72  0.301662  ...   0.01560   4.63   27.008333\n",
              "14   0.11000      4937.66  0.331662  ...   0.01900   5.06   29.294737\n",
              "15   0.13200      4932.23  0.363318  ...   0.02200   5.43   32.580000\n",
              "16   0.15900      4927.05  0.398748  ...   0.02700   5.18   30.504444\n",
              "17   0.19000      4921.48  0.435890  ...   0.03100   5.57   34.138710\n",
              "18   0.22900      4915.49  0.478539  ...   0.03900   5.99   35.172051\n",
              "19   0.27600      4907.10  0.525357  ...   0.04700   8.39   49.268936\n",
              "20   0.33200      4900.00  0.576194  ...   0.05600   7.10   42.092857\n",
              "21   0.39600      4892.37  0.629285  ...   0.06400   7.63   47.210625\n",
              "22   0.47700      4884.14  0.690652  ...   0.08100   8.23   48.465556\n",
              "23   0.56800      4875.29  0.753658  ...   0.09100   8.85   55.239560\n",
              "24   0.68500      4865.77  0.827647  ...   0.11700   9.52   55.736752\n",
              "25   0.83600      4855.52  0.914330  ...   0.15100  10.25   56.748344\n",
              "26   0.99500      4844.48  0.997497  ...   0.15900  11.04   69.086792\n",
              "27   1.20000      4832.60  1.095445  ...   0.20500  11.88   69.541463\n",
              "28   1.44600      4819.82  1.202497  ...   0.24600  12.78   75.121463\n",
              "29   1.74000      4806.06  1.319091  ...   0.29400  13.76   81.436735\n",
              "30   2.10000      4793.43  1.449138  ...   0.36000  12.63   73.675000\n",
              "31   2.50000      4779.98  1.581139  ...   0.40000  13.45   84.062500\n",
              "32   3.02000      4763.17  1.737815  ...   0.52000  16.81   97.627308\n",
              "33   3.63000      4747.75  1.905256  ...   0.61000  15.42   91.761639\n",
              "34   4.38000      4731.33  2.092845  ...   0.75000  16.42   95.892800\n",
              "35   5.21000      4713.83  2.282542  ...   0.83000  17.50  109.849398\n",
              "36   6.29000      4698.38  2.507987  ...   1.08000  15.45   89.981944\n",
              "37   7.58000      4678.74  2.753180  ...   1.29000  19.64  115.404031\n",
              "38   9.14000      4661.40  3.023243  ...   1.56000  17.34  101.594615\n",
              "39  11.02000      4643.13  3.319639  ...   1.88000  18.27  107.093298\n",
              "40  13.12000      4623.87  3.622154  ...   2.10000  19.26  120.329143\n",
              "41  15.81000      4607.71  3.976179  ...   2.69000  16.16   94.977546\n",
              "42  19.07000      4586.54  4.366921  ...   3.26000  21.17  123.838006\n",
              "43  21.99000      4568.78  4.689350  ...   2.92000  17.76  133.747397\n",
              "44  27.72000      4550.26  5.264979  ...   5.73000  18.52   89.594136\n",
              "45  32.99000      4530.93  5.743692  ...   5.27000  19.33  121.005066\n",
              "46  39.77000      4510.79  6.306346  ...   6.78000  20.14  118.136844\n",
              "47  47.94000      4495.11  6.923872  ...   8.17000  15.68   92.007246\n",
              "48  57.80000      4473.44  7.602631  ...   9.86000  21.67  127.031034\n",
              "49  68.80000      4456.55  8.294577  ...  11.00000  16.89  105.639273\n",
              "50  82.95000      4433.21  9.107689  ...  14.15000  23.34  136.823534\n",
              "51  97.50000      4408.86  9.874209  ...  14.55000  24.35  163.170103\n",
              "\n",
              "[52 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I55HpCh6SaEn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "outputId": "a937c3d3-c77b-4ffe-f4cf-d53e58da0d35"
      },
      "source": [
        "plt.figure(figsize=(12,5))\n",
        "plt.style.use('default')\n",
        "\n",
        "plt.loglog(df['t'], df['t*d(dP)/dt'],linewidth=2, marker='o', label = 'Derivative')\n",
        "plt.loglog(df['t'], df['dP'],linewidth=2, marker='o',label='Del(P)')\n",
        "\n",
        "plt.axhline(y=100, color='green',linewidth=3, label='IARF Reference')\n",
        "\n",
        "plt.title('Log-Log plot for a Hydraulic-Fractured Well')\n",
        "\n",
        "plt.xlabel('time(hours)')\n",
        "plt.ylabel('Pressure Related Axis')\n",
        "\n",
        "plt.legend(loc='best')\n",
        "\n",
        "\n",
        "plt.grid()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XLkSGBHhTHEY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# around 0.1 to 100 hours, we have a good scene to plot conventional."
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i3Yfe86QZfdw",
        "colab_type": "text"
      },
      "source": [
        "Bravo! Radial flow around y = 100 value.\n",
        "\n",
        "Linear flow around 0.1 to 1 hours\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yuTsUW10aP5E",
        "colab_type": "text"
      },
      "source": [
        "#Finding K from the radial flow regime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5J-Xv0wIaPM3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7dd3d5e9-b47f-4ae7-b6b8-8345a4c41c50"
      },
      "source": [
        "#100 = 70.6*q*mu*B/kh\n",
        "\n",
        "k = 70.6*q*mu*Bo/100/h\n",
        "\n",
        "print(f'The Permeability of un-altered Reservoir is : {k} mD!')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Permeability of un-altered Reservoir is : 12.708 mD!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TUBdXERKbGwm",
        "colab_type": "text"
      },
      "source": [
        "#Finding the Fracture Half Length."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kiFrTICQT4ia",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "hf = df[(df['t']>0.1) & (df['t']<1)][['t_1/2','FBHP (psia)']"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVWJs1Pzejpo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = hf['t_1/2']\n",
        "\n",
        "y = c*x + m"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3eXK8eKbmsv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "509a619b-4317-44f8-ea18-982328bd7b75"
      },
      "source": [
        "plt.plot(hf['t_1/2'], hf['FBHP (psia)'], marker='o', label='Field Data')\n",
        "plt.plot(x,y,label='Theoritical Estimate')\n",
        "\n",
        "plt.grid()\n",
        "\n",
        "plt.title('Pwf v/s t^1/2')\n",
        "plt.xlabel('t^1/2')\n",
        "plt.ylabel('Pwf(Psia)')\n",
        "\n",
        "plt.legend(loc='best')\n"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f6079405b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUAxmEX_boTz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "c , m = np.polyfit(hf['t_1/2'] , hf['FBHP (psia)'] , 1)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhqJ4qukcbw_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "67618d0e-b740-45f3-a15c-a2b1c2613b87"
      },
      "source": [
        "c , m"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-140.20313286400523, 4982.259877030024)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KqRPWLlIcr5Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The slope gives Fracture Half length.\n",
        "\n",
        "xf =(-1)*(4.06*q*Bo/(h*c))*np.sqrt(mu/k/phi/ct)"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqrwXOaidRtN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 108
        },
        "outputId": "53f83c5a-f5d8-4eac-8fc1-8ed3e44a0340"
      },
      "source": [
        "print('RESULTS of Analysis:\\n')\n",
        "print(f'1. Permeability of unaltered formation : {k} mD.\\n')\n",
        "print(f'2. Fracture Half Length : {xf} ft.')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "RESULTS of Analysis:\n",
            "\n",
            "1. Permeability of unaltered formation : 12.708 mD.\n",
            "\n",
            "2. Fracture Half Length : 141.64625986347212 ft.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30MOnsMGdS3c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}